A typical statistical validation of a measurement entails determining whether or not to reject a hypothesis that a measurement value is a member of a targeted population. Hypothesis testing is a useful technique that allows a measurement system designer to control a measurement process and the root causes that the measurements are intended to predict. However, a system designer may want to go beyond knowing simply that a particular measurement is within a single range of acceptable values. The system designer may want to know what an actual measurement value is, to a reasonable tolerance, knowing it is buried within a relatively wide acceptance range due to normal noise and variation.
A measurement typically contains natural variations that may obfuscate the information a measurement system is attempting to obtain from the measurement. The information is the signal and what is not information, but is added into the measurement with the signal, is noise. As such, a measurement system is required to estimate a signal in the presence of noise.